Spatial-spectral fingerprint spoof detection

ABSTRACT

Methods and apparatus are provided of deriving a discrimination feature set for use in identifying biometric spoofs. True skin sites are illuminated under distinct optical conditions and light reflected from each of the true skin sites is received. True-skin feature values are derived to characterize the true skin sites. Biometric spoofs is similarly illuminated under the distinct optical conditions and light reflected from the spoofs is received. Spoof feature values are derived to characterize the biometric spoofs. The derived true-skin feature values are compared with the derived spoof feature values to select a subset of the features to define the discrimination feature set.

CROSS REFERENCE TO RELATED APPLICATION

This application is related to concurrently filed, commonly assignedU.S. patent application Ser. No. 11/461,253, entitled “BIOMETRICS WITHSPATIOSPECTRAL SPOOF DETECTION,” the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

This application relates generally to biometrics. More specifically,this application relates to fingerprint spoof detection.

“Biometrics” refers generally to the statistical analysis ofcharacteristics of living bodies. One category of biometrics includes“biometric identification,” which commonly operates under one of twomodes to provide automatic identification of people or to verifypurported identities of people. Biometric sensing technologies measurethe physical features or behavioral characteristics of a person andcompare those features to similar prerecorded measurements to determinewhether there is a match. Physical features that are commonly used forbiometric identification includes faces, irises, hand geometry, veinstructure, and fingerprints. The last of these is the most prevalent ofall biometric-identification features. Currently, methods for analyzingcollected fingerprints include optical, capacitive, radio-frequency,thermal, ultrasonic, and several other less common techniques.

Biometric sensors, particularly fingerprint biometric sensors, aregenerally prone to being defeated by various forms of spoof samples. Inthe case of fingerprint readers, a variety of methods are known in theart for presenting readers with a fingerprint pattern of an authorizeduser that is embedded in some kind of inanimate material such as paper,gelatin, epoxy, latex, and the like. Thus, even if a fingerprint readercan be considered to reliably determine the presence or absence of amatching fingerprint pattern, it is also critical to the overall systemsecurity to ensure that the matching pattern is being acquired from agenuine, living finger, which may be difficult to ascertain with manycommon sensors.

There is accordingly a general need in the art for methods and systemsthat permit discrimination between legitimate and spoof presentations offingerprints.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the invention provide methods and systems that may beused in discriminating genuine skin sites presented in biometricapplications from spoofs. In a first set of embodiments, methods areprovided of deriving a discrimination feature set for use in identifyingbiometric spoofs. Each of a plurality of true skin sites is illuminatedunder a plurality of distinct optical conditions. First light reflectedfrom each of the true skin sites is received. True-skin feature valuesare derived for each of a plurality of features from the received firstlight to characterize the true skin sites. Each of a plurality ofbiometric spoofs is similarly illuminated under the plurality ofdistinct optical conditions. Second light reflected from each of thebiometric spoofs is received. Spoof feature values are derived for eachof the plurality of features from the received second light tocharacterize the biometric spoofs. The derived true-skin feature valuesare compared with the derived spoof feature values to select a subset ofthe features to define the discrimination feature set.

Because the spectral and spatial frequency features or combinations ofthese features that uniquely distinguish a true skin image from a spoofimage may not be readily apparent or identified by visual comparison ofthe features, some embodiments rely on discriminant-analysis techniquesto first train a device to identify spatial and spectral features thatare unique to true skin features and spatial and spectral frequencyfeatures that are unique to spoof features. A comparison is made of suchfeatures to new spectral and spatial frequency data at the time ofattempted spoof detection. Discriminant-analysis methods that may beincorporated include those based on Mahalanobis distances, spectralresidual magnitudes, K-nearest-neighbor methods, or linear or nonlineardiscriminant techniques to compare spectral and spatial frequency dataacquired from an individual with spatial and spectral frequency datapresent in a database.

In some embodiments, the true-skin feature values are derived byextracting a plurality of true-skin images from the received first lightfor each of the true skin sites, and the spoof feature values arederived by extracting a plurality of spoof images from the receivedsecond light for each of the biometric spoofs. Each of these true-skinimages and each of these spoof images correspond to an image under oneof the plurality of distinct optical conditions. Derivation of thetrue-skin feature values may further comprise a decomposition of thetrue-skin images into a plurality of different spectral frequencycomponents, with the spoof feature values further being derived bydecomposition of each of the spoof images into the plurality ofdifferent spectral frequency components.

Decomposition into spatial frequency components of each of the true-skinimages and of the spoof images may sometimes comprise performing awavelet decomposition. In addition, in some embodiments, a ratio offirst of the different spatial frequency components for the true-skinimages to a second of the different spatial frequency components for thetrue-skin images may be calculated. Similarly, a ratio of a first of thedifferent spatial frequency components for the spoof images to a secondof the different spatial frequency components for the spoof images maybe calculated.

In certain embodiments, an intensity distribution is calculated for eachof the different spatial frequency components for the true-skin imagesand for the spoof images. In such cases, at least one of the featuresmay be substantially invariant to illumination intensity. An example ofsuch an illumination-intensity invariant feature is a ratio of anintensity at a first predetermined percentile of an intensitydistribution to a second predetermined percentile of the intensitydistribution. In other cases, at least one of the features may vary withillumination intensity. An example of such an illumination intensityvariant feature is a different between the intensity at the firstdetermined percentile ad the intensity at the second predeterminedpercentile.

A number of different techniques may be used in different embodiments tocompare the derived true-skin feature values with the derived spooffeature values. For example, in one embodiment, the true skin sites andthe biometric spoofs define separate classes. The comparison includescalculating rations of within-class variance to between-class variancefor a quantity derived from the features. In one instance, the quantityderived from the features comprises a Fisher linear discriminanttransform of the features.

Selection of the subset of the features may also be performed with avariety of different techniques in different embodiments. Examples ofsuch techniques include learning algorithms like genetic and otheralgorithms.

In a second set of embodiments, methods are provided of performing abiometric function on a purported skin site. The purported skin site isilluminated under a plurality of distinct optical conditions. Lightscattered from the purported skin site is received. A feature value foreach of a plurality of features is derived from the received light. Acomparison is performed of the derived feature value for each of theplurality of features with reference feature values. Whether thepurported skin site is a true skin site is accordingly determined fromthe comparison.

Specific techniques similar to those used in deriving the discriminationfeature set may also be applied in deriving the feature value. Forinstance, a plurality of images may be extracted from the receivedlight, with each of the images corresponding to an image under one ofthe plurality of distinct optical conditions. Each of the plurality ofimages may be decomposed into a plurality of different spatial frequencycomponents. For instance, the decomposition may be achieved byperforming a wavelet decomposition. In one embodiment, a ratio of afirst of the different spatial frequency components to a second of thedifferent spatial frequency components is also calculated. An intensitydistribution may be calculated for each of the different spatialfrequency components. In some cases, at least one of the features issubstantially invariant to illumination intensity, such as for a featurethat comprises a ratio of an intensity at a first predeterminedpercentile of the intensity distribution to an intensity at a secondpredetermined percentile of the intensity distribution. In other cases,at least one of the features varies with illumination intensity, such asfor a feature that comprises a different between the intensity at thefirst predetermined percentile of the intensity distribution and theintensity at the second predetermined percentile of the intensitydistribution.

In another embodiment, the purported skin site is illuminated under aplurality of distinct optical conditions. Light reflected from thepurported skin site is received. The received light is used to perform abiometric identification as well as for determining whether thepurported skin site is true skin or a spoof.

In cases where it is determined that the purported skin site is not atrue skin site, an alarm maybe issued to identify the purported skinsite as a spoof. In some embodiments, a biometric identification is alsoperformed from the received light.

The methods of the invention may also be embodied on various types ofapparatus. For instance, a computer-readable storage medium may beprovided having a computer-readable program for directing operation of acomputational device. The computational device includes a processor incommunication with a storage device. The computer-readable program hasinstructions for implementing any of the methods described.

In other cases, a biometric sensor may be provided. The biometric sensorhas an illumination subsystem, a detection subsystem, and a controller.The illumination subsystem is disposed to illuminate a purported skinsite of an individual. The detection subsystem is disposed to receivelight scattered from the purported skin site. The controller is incommunication with the illumination subsystem and with the detectionsubsystem, and has instructions for implementing any of the methodsdescribed above.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and the drawings wherein like reference labels are usedthroughout the several drawings to refer to similar components. In someinstances, reference labels include a numerical portion followed by alatin-letter suffix; reference to only the numerical portion ofreference labels is intended to refer collectively to all referencelabels that have that numerical portion but different latin-lettersuffices.

FIG. 1 provides a front view of a multispectral biometric sensor in oneembodiment of the invention;

FIG. 2 provides a schematic representation of a computer system that maybe used to manage functionality of the multispectral biometric sensorillustrated in FIG. 1 and/or to implement various methods of theinvention;

FIGS. 3A and 3B provide a comparison of fingerprint measurements madefrom a living finger and made from a prosthetic that acts as a nonlivingspoof;

FIG. 4 is a flow diagram summarizing methods for identifying featuresfor discriminating between legitimate and spoof presentations offingerprints;

FIG. 5 illustrates a multispectral datacube derived from data collectedwith a multispectral biometric sensor like that illustrated in FIG. 1;

FIG. 6 provides an illustration of a wavelet decomposition obtained byapplication of high-pass and low-pass filters to an image;

FIGS. 7A and 7B provide a comparison of high-frequency and low-frequencyimages of a fingerprint derived through decomposition of themultispectral datacube of FIG. 5;

FIGS. 8A and 8B illustrate differences between conventional and integralhistograms;

FIG. 9 provides an integral histogram for low-frequency images derivedfrom a decomposition of the multispectral datacube of FIG. 5 in oneembodiment;

FIG. 10 provides an integral histogram for a ratio of low-frequency tohigh-frequency images derived from a decomposition of the multispectraldatacube of FIG. 5 in one embodiment;

FIG. 11 provides a ratio of between-class variance to in-class variancefor a number of different features derived from a decomposition of themultispectral datacube of FIG. 5 in one embodiment;

FIG. 12 provides a ratio of between-class variance to in-class variancefor a number of different features derived from application of a Fisherlinear discriminant to a decomposition of the multispectral datacube ofFIG. 5 in one embodiment;

FIG. 14 is a scatter plot illustrating the ability of two Fisherfeatures derived in accordance with an embodiment of the invention todiscriminate between legitimate and spoof presentations of fingerprints;and

FIG. 14 is a flow diagram summarizing methods for discriminating betweenlegitimate and spoof presentations of fingerprints through a comparisonof discriminating features.

DETAILED DESCRIPTION OF THE INVENTION

1. Introduction

Embodiments of the invention provide methods and systems that may beused to discriminate between legitimate and spoof presentations offingerprints. As used herein, the term “fingerprints” is intended torefer to any topographical skin feature, irrespective of whether itoccurs on a finger or on another part of the body. It is generallyexpected that applications of the invention will find most utility whenapplied to topographical features present on the volar surfaces offingers or hands, but the methods and systems described herein are notrestricted to such skin locations and may be applied to other skinlocations. Specific examples of skin sites from which “fingerprints” maybe extracted thus include all surfaces and all joints of the fingers andthumbs, the fingernails and nail beds, the palms, the backs of thehands, the wrist and forearms, the face, the ears, areas around theeyes, and all other external surfaces of the body.

The ability to discriminate between legitimate and spoof presentationsof fingerprints according to embodiments of the invention is based ondifferences in the combined spatial and spectral properties of livingskin sites when compared with spoofs. In particular, skin is a complexorgan made up of multiple layers, various mixtures of chemicals, anddistinct structures such as hair follicles, sweat glands, and capillarybeds. The outermost layer of skin, the epidermis, is supported by theunderlying dermis and hypodermis. The epidermis itself may have fiveidentified sublayers that include the stratum corneum, the stratumlucidum, the stratum granulosum, the stratum spinosum, and the stratumgerminativum. Thus, for example, the skin below the top-most stratumcorneum has some characteristics that relate to the surface topography,as well as some characteristics that change with depth into the skin.While the blood supply to skin exists in the dermal layer, the dermishas protrusions into the epidermis known as “dermal papillae,” whichbring the blood supply close to the surface via capillaries. In thevolar surfaces of the fingers, this capillary structure follows thestructure of the friction ridges on the surface. In other locations onthe body, the structure of the capillary bed may be less ordered, but isstill characteristic of the particular location and person. As well, thetopography of the interface between the different layers of skin isquite complex and characteristic of the skin location and the person.

While spoofs may sometimes be made with considerable complexity, theirstructure of skin remains much more complex in both its spectral andspatial properties. In particular, spoofs have much simpler spectralproperties and their spatial texture tends to be uniform with spectra.This may be contrasted with skin sites, which provide complex spectralproperties in combination with a complex interplay between spatialtexture and optical spectra, with nonuniformities existing in a spatialsense in addition to a spectral sense. These differences provide a basisfor discrimination that may be embraced by the concept of “chromatictexture.” This is an extension of the concept of “image texture,” whichrefers generally to any of a large number of metrics that describe someaspect of a spatial distribution of tonal characteristics of an image.For example, some textures, such as those commonly found in fingerprintpatterns or wood grain, are flowlike and may be well described bymetrics such as an orientation and coherence. “Chromatic texture”extends this concept as a statistical distribution that is additionallya function of spectral frequency. Certain statistical moments such asmean, variance, skew, and kurtosis may be used in quantitativedescriptions of texture. Chromatic texture may be manifested byvariations in pixel intensities at different spectral frequencies acrossan image, which may be used in embodiments of the invention to identifyspoofs in biometric applications.

2. Data Collection

Chromatic texture information may be acquired in embodiments of theinvention by collecting an image of a purported skin site undermultispectral conditions. As used herein, “multispectral” data refers todata that are collected during a single illumination session under aplurality of distinct optical conditions. The different opticalconditions may include differences in polarization conditions,differences in illumination angle, differences in imaging angle, anddifferences in wavelength. One embodiment of a multispectral biometricsensor that may be used to collect multispectral data is shown in frontview in FIG. 1. In this illustration, the multispectral sensor 101comprises an illumination subsystem 121 having one or more light sources103 and a detection subsystem 123 with an imager 115.

The figure depicts an embodiment in which the illumination subsystem 121comprises a plurality of illumination subsystems 121 a and 121 b, butthere is no limitation on the number of illumination or detectionsubsystems 121 or 123 that may be used. For example, the number ofillumination subsystems 121 may conveniently be selected to achievecertain levels of illumination, to meet packaging requirements, and tomeet other structural constraints of the multispectral biometric sensor101. Illumination light passes from the source 103 through illuminationoptics 105 that shape the illumination to a desired form, such as in theform of flood light, light lines, light points, and the like. Theillumination optics 105 are shown for convenience as consisting of alens but may more generally include any combination of one or morelenses, one or more mirrors, and/or other optical elements. Theillumination optics 105 may also comprise a scanner mechanism (notshown) to scan the illumination light in a specified one-dimensional ortwo-dimensional pattern. The light source 103 may comprise a pointsource, a line source, an area source, or may comprise a series of suchsources in different embodiments. In one embodiment, the illuminationlight is provided as polarized light, such as by disposing a linearpolarizer 107 through which the light passes before striking a finger119 or other skin site of the person being studied.

In some instances, the light source 103 may comprise one or morequasimonochromatic sources in which the light is provided over a narrowwavelength band. Such quasimonochromatic sources may include devicessuch as light-emitting diodes, laser diodes, or quantum-dot lasers.Alternatively, the light source 103 may comprise a broadband source suchas an incandescent bulb or glow bar. In the case of a broadband source,the illumination light may pass through a bandpass filter 109 to narrowthe spectral width of the illumination light. In one embodiment, thebandpass filter 109 comprises one or more discrete optical bandpassfilters. In another embodiment, the bandpass filter 109 comprises acontinuously variable filter that moves rotationally or linearly (orwith a combination of rotational and linear movement) to change thewavelength of illumination light. In still another embodiment, thebandpass filter 109 comprises a tunable filter element such as aliquid-crystal tunable filter, an acousto-optical tunable filter, atunable Fabry-Perot filter or other filter mechanism known to oneknowledgeable in the art.

After the light from the light source 103 passes through theillumination optics 105, and optionally the optical filter 109 and/orpolarizer 107, it passes through a platen 117 and illuminates the finger119 or other skin site. The sensor layout and components mayadvantageously be selected to minimize the specular reflection of theillumination into the detection optics 113. In one embodiment, suchspecular reflections are reduced by relatively orienting theillumination subsystem 121 and detection subsystem 123 such that theamount of directly reflected light detected is minimized. For instance,optical axes of the illumination subsystem 121 and the detectionsubsystem 123 may be placed at angles such that a mirror placed on theplaten 117 does not reflect an appreciable amount of illumination lightinto the detection subsystem 123. In addition, the optical axes of theillumination and detection subsystems 121 and 123 may be placed atangles relative to the platen 117 such that the angular acceptance ofboth subsystems is less than the critical angle of the system; such aconfiguration avoids appreciable effects due to total internalreflectance between the platen 117 and the skin site 119.

An alternative mechanism for reducing the specular reflected light makesuse of optical polarizers. Both linear and circular polarizers can beemployed advantageously to make the optical measurement more sensitiveto certain skin depths, as known to one familiar in the art. In theembodiment illustrated in FIG. 1, the illumination light is polarized bylinear polarizer 107. The detection subsystem 123 may then also includea linear polarizer 111 that is arranged with its optical axissubstantially orthogonal to the illumination polarizer 107. In this way,light from the sample must undergo multiple scattering events tosignificantly change its state of polarization. Such events occur whenthe light penetrates the surface of the skin and is scattered back tothe detection subsystem 123 after many scatter events and it is onlythis light that finds its way to the detection system, the orthogonallypolarized light from any specular reflection being rejected by thedetection subsystem polarizer 111.

The detection subsystem 123 may incorporate detection optics thatcomprise lenses, mirrors, and/or other optical elements that form animage of the region near the platen surface 117 onto the detector 115.The detection optics 113 may also comprise a scanning mechanism (notshown) to relay portions of the platen region onto the detector 115 insequence. In all cases, the detection subsystem 123 is configured to besensitive to light that has penetrated the surface of the skin andundergone optical scattering within the skin and/or underlying tissuebefore exiting the skin.

The illumination subsystem 121 and detection subsystem 123 may beconfigured to operate in a variety of optical regimes and at a varietyof wavelengths. One embodiment uses light sources 103 that emit lightsubstantially in the region of 400-1000 nm; in this case, the detector115 may be based on silicon detector elements or other detector materialknown to those of skill in the art as sensitive to light at suchwavelengths. In another embodiment, the light sources 103 may emitradiation at wavelengths that include the near-infrared regime of1.0-2.5 μm, in which case the detector 115 may comprise elements madefrom InGaAs, InSb, PbS, MCT, and other materials known to those of skillin the art as sensitive to light at such wavelengths.

The structure of the device illustrated in FIG. 1 is merely exemplaryand a variety of other structures may be used in other embodiments tocollect multispectral data. Some examples of alternative structures thatmay be used are described in the following copending, commonly assignedapplications, the entire disclosure of each of which is incorporatedherein by reference for all purposes: U.S. Prov. Pat. Appl. No.60/483,281, entitled “HYPERSPECTRAL FINGERPRINT READER,” filed Jun. 27,2003; U.S. Prov. Pat. No. 60/504,594, entitled “HYPERSPECTRALFINGERPRINTING,” filed Sep. 18, 2003; U.S. Prov. Pat. No. 60/552,662,entitled “OPTICAL SKIN SENSOR FOR BIOMETRICS,” filed Mar. 10, 2004; U.S.Prov. patent application Ser. No. 10/576,364, entitled “MULTISPECTRALFINGER RECOGNITION,” filed Jun. 1, 2004 by Robert K. Rowe; 60/600,867,entitled “MULTISPECTRAL IMAGING BIOMETRIC,” filed Aug. 11, 2004; U.S.Prov. Pat. Appl. No. 60/610,802, entitled “FINGERPRINT SPOOF DETECTIONUSING MULTISPECTRAL IMAGING,” filed Sep. 17, 2004; U.S. Prov. Pat. Appl.No. 60/654,354, entitled “SYSTEMS AND METHODS FOR MULTISPECTRALFINGERPRINT SENSING,” filed Feb. 18, 2005; U.S. Prov. Pat. Appl. No.60/659,024, entitled “MULTISPECTRAL IMAGING OF THE FINGER FORBIOMETRICS,” filed Mar. 4, 2005; U.S. Prov. Pat. Appl. No. 60/675,776,entitled “MULTISPECTRAL BIOMETRIC SENSORS,” filed Apr. 27, 2005; U.S.patent application Ser. No. 10/818,698, entitled “MULTISPECTRALBIOMETRIC SENSOR,” filed Apr. 5, 2004 by Robert K. Rowe et al.; U.S.patent application Ser. No. 11/437,388, entitled “MULTISPECTRALBIOMETRIC SENSOR,” filed May 18, 2006 by Robert K. Rowe et al; U.S.patent application Ser. No. 11/383,901, entitled “BIOMETRIC SENSOR,”filed May 17, 2006 by Robert K. Rowe et al; U.S. patent application Ser.No. 11/177,817, entitled “LIVENESS SENSOR,” filed Jul. 8, 2005 by RobertK. Rowe; U.S. patent application Ser. No. 11/115,100, entitled“MULTISPECTRAL IMAGING BIOMETRICS,” filed Apr. 25, 2005; U.S. patentapplication Ser. No. 11/115,101, entitled “MULTISPECTRAL BIOMETRICIMAGING,” filed Apr. 25, 2005; U.S. patent application Ser. No.11/115,075, entitled “MULTISPECTRAL LIVENESS DETERMINATION,” filed Apr.25, 2005; U.S. patent application Ser. No. 11/015,732, entitled“COMBINED TOTAL-INTERNAL-REFLECTANCE AND TISSUE IMAGING SYSTEMS ANDMETHODS,” filed Dec. 17, 2004 by Robert K. Rowe; U.S. patent applicationSer. No. 11/379,945, entitled “MULTISPECTRAL BIOMETRIC SENSORS,” filedApr. 24, 2006 by Robert K. Rowe; and U.S. patent application Ser. No.11/219,006, entitled “COMPARATIVE TEXTURE ANALYSIS OF TISSUE FORBIOMETRIC SPOOF DETECTION,” filed Sep. 1, 2005 by Robert K. Rowe.

Furthermore, a multispectral biometric sensor like that illustrated inFIG. 1 or as described in other applications may be used in embodimentsof the invention in combination with other types of biometric sensors.For example, a configuration might use thermal, ultrasonic,radio-frequency, or other mechanism to collect information defining afingerprint pattern of a purported skin site for comparison with adatabase, while simultaneously collecting multispectral data to aid inspoof detection. In other embodiments, the multispectral biometricsensor is advantageously used to collect a set of multispectral datathat are used in both biometric identification and in spoof detection.

Operation of the multispectral sensor may be coordinated with acomputational system like that shown schematically in FIG. 2. Thedrawing broadly illustrates how individual system elements may beimplemented in a separated or more integrated manner. The computationaldevice 200 is shown comprised of hardware elements that are electricallycoupled via bus 226, which is also coupled with the multispectralbiometric sensor 101. The hardware elements include a processor 202, aninput device 204, an output device 206, a storage device 208, acomputer-readable storage media reader 210 a, a communications system214, a processing acceleration unit 216 such as a DSP or special-purposeprocessor, and a memory 218. The computer-readable storage media reader210 a is further connected to a computer-readable storage medium 210 b,the combination comprehensively representing remote, local, fixed,and/or removable storage devices plus storage media for temporarilyand/or more permanently containing computer-readable information. Thecommunications system 214 may comprise a wired, wireless, modem, and/orother type of interfacing connection and permits data to be exchangedwith external devices.

The computational device 200 also comprises software elements, shown asbeing currently located within working memory 220, including anoperating system 224 and other code 222, such as a program designed toimplement methods of the invention. It will be apparent to those skilledin the art that substantial variations may be used in accordance withspecific requirements. For example, customized hardware might also beused and/or particular elements might be implemented in hardware,software (including portable software, such as applets), or both.Further, connection to other computing devices such as networkinput/output devices may be employed.

3. Data Analysis

The potential for spoofs to be effective in circumventing conventionalbiometric analyses is illustrated with FIGS. 3A and 3B, which showfingerprint images taken from a finger and from a spoof respectively.The spoof that provided the image in FIG. 3B was an ultrarealisticprosthetic fingertip whose construction was commissioned by theinventors. The prosthetic fingertip was made of a multilayer siliconestructure, cast on a real and available finger, and colored to match thecoloring of the real finger. Fine detail was included on the prosthetic,including the fine detail of fingerprints. It is apparent from theimages in FIGS. 3A and 3B that it is difficult to discern which imagewas collected from a real finger and which was collected with a spoof.

a Identification of Discrimination Features

FIG. 4 provides a flow diagram that summarizes methods for identifyingfeatures that may be used as discriminants in identifying spoofs.Generally, after the identification of suitable discriminants, analysisof purported skin sites may be performed by a suitable comparison offeatures with the discriminants.

The method begins at block 404 with the illumination of a true skin siteunder multispectral conditions, as may be performed using amultispectral biometric sensor like that described above. The collecteddata may be considered to define a multispectral dataset that permitsextraction of information along a number of independent variables. Themultispectral dataset is sometimes referred to as a “multispectraldatacube,” but this terminology is not intended to suggest anyparticular limit on the number of independent variables embraced by thedataset; the number of independent variables depends on the number ofdifferent factors used in generating the different optical conditionsdefining the multispectral conditions under which data are collected andmay vary among different embodiments.

As indicated at block 408, a plurality of image frames are extractedfrom the multispectral datacube to correspond to different opticalconditions. This is illustrated schematically in FIG. 5, in which amultispectral datacube has been used to extract an image set 500 thatconsists of eight images 504. The number of images extracted may vary indifferent embodiments. Merely by way of example, the eight images 504might correspond to images under two different polarizationconditions—unpolarized and cross-polarized conditions—for each of fourdifferent illumination wavelengths. In other instances, the differentimages might correspond to different illumination angles, differentimaging angles, and/or any other differences in optical conditions.

Each of the image frames is decomposed into different spatial frequencycomponents at block 412. There are a number of different ways in whichsuch a decomposition may be accomplished in different embodiments. Incertain embodiments, a wavelet transform is applied to each of the imageframes. This may be done in embodiments that use a discrete wavelettransform by applying high-pass and low-pass filters to the image framesas illustrated in FIG. 6 according to a Mallet-tree decomposition. Inthis type of decomposition, an initial image frame S({right arrow over(x)}) is subjected to the high-pass filter 604 to produce S_(H)({rightarrow over (x)}) and is subject to the low-pass filter 608 to produceS_(L)({right arrow over (x)}). Successive decompositions, if desired,are applied to the output of the low-pass filter 608. Thus, a secondlevel of decompositions is applied to S_(L)({right arrow over (x)}) toproduce S_(LH)({right arrow over (x)}) and S_(LL)({right arrow over(x)}). This may be repeated for as many levels of decomposition asdesired, with an nth level of decomposition resulting in the generationof (n+1) signals.

At each decomposition level, the filters produce signals that span aportion of the original frequency range. In the illustration of FIG. 6,three levels of decomposition result in the generation of four signals,with S_(H)({right arrow over (x)}) representing a high-frequency signal,S_(LH)({right arrow over (x)}) representing a medium-frequency signal,S_(LLH)(x) representing a low-frequency signal, and S_(LLL)({right arrowover (x)}) representing a very-low-frequency signal. A comparison isprovided in FIGS. 7A and 7B of signals in different frequency regions toillustrate the different types of information available at thosefrequencies, with FIG. 7A providing a high-frequency image and FIG. 7Bproviding a low-frequency image. The results of FIGS. 7A and 7B werederived from the same original image.

The particular form of the high-pass and low-pass filters 604 and 608may vary in different embodiments. For example, in one embodiment whereHaar transforms are implemented, the high-pass filter 604 effectivelyacts to determine a difference between adjacent pixels of the inputimage while the low-pass filter 608 effectively acts to determine anaverage between adjacent pixels. Other examples of transforms that maybe implemented with discrete wavelets, and which are well-known to thoseof skill in the art, include Daubechies transforms, Coiflet transforms,Symlet transforms, Meyer transforms, Morlet transforms, and mexican-hattransforms, among others. The implementation of these and othertransforms are also within the scope of the invention.

In particular embodiments, the frequency decomposition performed atblock 412 is performed with a dual-tree complex wavelet transform,details of which are provided in Nick Kingsbury, “Complex Wavelets forShift Invariant Analysis and Filtering of Signals,” J. Appl. Comp.Harmonic Analysis, 10, 234 (2001), the entire disclosure of which isincorporated herein by reference for all purposes. Briefly, theextension of wavelet analysis to a complex domain increases thedimensionality of the analysis. Instead of outputting two images by theapplication of filters, each level of decomposition produces four imagesequal in size to the input image for that level, with thelowest-frequency image becoming the input for the next level. Each ofthe images is constructed using different row and column filters, sothat the output images are themselves provided in the form of fourcomponent images, each of which is one quarter the size of the inputimage. In each instance, the four component images are encoded in pixelquads. This technique advantageously has the property that it isrelatively spatially invariant, particularly in comparison with avariety of other types of techniques.

Other examples of techniques that may be used to effect the frequencydecomposition in different embodiments include the use of moving-windowFourier transforms and the application of Gabor filters, among a varietyof different techniques known to those of skill in the art.

Returning to FIG. 4, the decomposed images may then each be used tocalculate an intensity-distribution feature set. Generally, elements ofthe intensity-distribution feature set include scalar values thatquantify some aspect of each of the decomposed images. In certainembodiments, this is accomplished through the construction of integralhistograms from each of the decomposed images, with the scalar valuesbeing determined from relationships between different points in theintegral histogram.

FIGS. 8A and 8B provide an illustration of the differences betweenclassical histograms and integral histograms. While a classicalhistogram like that shown in FIG. 8A provides the frequency with which avariable appears between two defined values, an integral histogram likethat shown in FIG. 8B provides the value at any percentile of thedistribution. Information characterizing a distribution may generally bepresented in either form; but for the applications described herein, anintegral distribution has the advantage that the ratio of any twopercentile values is substantially constant with respect to gain-likevariables that multiply all values uniformly. One example of such again-like variable is the illumination intensity in the multispectralbiometric system. This renders scalar feature-set variables that takethe form of ratios of percentile values substantially invariant toillumination intensity.

In addition to scalar features that are ratios of percentile values,other arithmetic combinations of percentile values may be used as scalarfeatures. These other arithmetic combinations may in some instances notbe invariant to illumination intensity, but may nonetheless sometimesprovide valuable discriminant information. Merely by way of example, onescalar feature that may be determined for each of the decomposed imagesis the ratio of the intensity of the image at percentile 0.30 to theintensity of the image at percentile 0.70. Another scalar feature thatmay be determined is the sum of the intensity of the image at percentile0.30 with the intensity of the image at percentile 0.70. The use of 0.30and 0.70 percentiles in these examples is made purely for illustrativepurposes. In other instances, different percentile values may be used.Also, the invention is not limited by the number of scalar featuresderived from each of the images. In some instances, only a singlefeature might be derived from each image, while other embodiments mayderive a plurality of features. Furthermore, it is not necessary thatscalar features be derived from every image that results from thedecomposition. In some embodiments, scalar features are extracted from asubset of the decomposed images. Also, while the example discussedherein make use of scalar features, it is possible in alternativeembodiments to define features that have a multidimensional quality, orto combine the scalar features into a multidimensional vector.

The method embraced by blocks 404-416 of FIG. 4 may be repeated formultiple skin sites, with block 420 of the drawing indicating that themethod loops until all skin sites of a set have been processed in thisway.

A similar procedure may be applied to multiple spoofs, with the variousspoofs preferably having diverse characteristics representative of thetypes of spoofs that might be attempted. The same basic methodology isapplied to the spoofs as was applied to the skin sites. At block 424, aparticular spoof is illuminated under multispectral conditions. Thesemultispectral conditions may be substantially the same multispectralconditions under which the true skin sites were illuminated at block404. A plurality of image frames of the spoof that correspond todifferent optical conditions are extracted from the resulting datacubeat block 428. Each of the image frames is decomposed into differentfrequency components at block 432 and an intensity distribution featureset is calculated from each frame at block 436. These steps may beperformed using the same techniques applied to the true skin sites, andmay be performed for a number of different spoofs as indicated with thecheck performed at block 440.

After feature sets have been generated from both skin sites and fromspoofs, a discriminant model is applied at block 444 to determinediscriminating features from the feature sets. There are a number ofdifferent types of discriminant models that may be applied in differentembodiments. Certain embodiments make use of the recognition by theinventors that, on average, spoof and true skin sites will havedifferent intensity distributions. This is a consequence of thedifferent structural characteristics that distinguish living tissue andare manifested in both spectral and spatial variations. For anyparticular feature, the variance between spoof classes and a trueskin-site class is expected to be small relative to the within-classvariances. Thus, one measure of the discriminating power of the derivedfeatures is the ratio of within-class variance to between-classvariance. In certain embodiments, this ratio is thus calculated directlywhen applying the discriminant model at block 444.

For example, applying steps 404-416 for a particular true skin site mayprovide a number of feature values t₁ ⁽¹⁾, t₂ ⁽¹⁾, . . . , t_(N) ⁽¹⁾,where N is the number of features. Representing this set of featurevalues as an N-dimensional vector {right arrow over (t)}⁽¹⁾, the set offeatures for all the measurements on true skin sites may be representedby the set of vectors {right arrow over (t)}⁽¹⁾, {right arrow over(t)}⁽²⁾, . . . , {right arrow over (t)}^((M) ^(t) ⁾, where M_(t) is thenumber of multispectral measurements performed on true skin sites.Similarly, the set of features for all the measurements on spoofs may berepresented by the set of N-dimensional vectors {right arrow over(s)}⁽¹⁾, {right arrow over (s)}⁽²⁾, {right arrow over (s)}^((M) ^(s) ⁾,where M_(s) is the number of multispectral measurements on spoofs. Forthis set of feature values, the mean of the true-skin-site featurevalues is

${{\overset{\rightharpoonup}{\mu}}_{t} = {\frac{1}{M_{t}}{\sum\limits_{k = 1}^{M_{t}}\;{\overset{\rightharpoonup}{t}}^{(k)}}}},$the mean of the spoof feature values is

${{\overset{\rightharpoonup}{\mu}}_{s} = {\frac{1}{M_{s}}{\sum\limits_{k = 1}^{M_{s}}\;{\overset{\rightharpoonup}{s}}^{(k)}}}},$and the mean of the entire set of feature values is

$\overset{\rightharpoonup}{\mu} = {\frac{1}{M_{t} + M_{s}}{\left( {{\sum\limits_{k = 1}^{M_{t}}\;{\overset{\rightharpoonup}{t}}^{(k)}} + {\sum\limits_{k = 1}^{M_{s}}\; s^{(k)}}} \right).}}$The within-class variance is

$\sigma_{WC}^{{(j)}2} = {\frac{1}{M_{t} + M_{s}}\left( {{\sum\limits_{k = 1}^{M_{t}}\;\left( {\mu_{t}^{(j)} - t_{j}^{(k)}} \right)^{2}} + {\sum\limits_{k = 1}^{M_{t}}\;\left( {\mu_{s}^{(j)} - s_{j}^{(k)}} \right)^{2}}} \right)}$and the between-class variance is

${\sigma_{BC}^{{(j)}2} = {\frac{1}{M_{t} + M_{s}}\left( {{\sum\limits_{k = 1}^{M_{t}}\;\left( {\mu^{(j)} - t_{j}^{(k)}} \right)^{2}} + {\sum\limits_{k = 1}^{M_{t}}\;\left( {\mu^{(j)} - s_{j}^{(k)}} \right)^{2}}} \right)}},$permitting calculation of the ratio as

$R = \frac{\sigma_{WC}^{{(j)}2}}{\sigma_{BC}^{{(j)}2}}$for each feature j.

In other embodiments, a Fisher linear discriminant may be applied totransform the raw derived features into a new set of features. This isaccomplished by applying a transform T to the feature sets {right arrowover (t)} and {right arrow over (s)} to produce new feature sets{right arrow over (t)}′=T{right arrow over (t)} and {right arrow over(s)}′=T{right arrow over (s)}.The transform is an N×N matrix that may be expressed as T=[{right arrowover (e)}₁, {right arrow over (e)}₂, . . . , {right arrow over(e)}_(N)], where the set of {right arrow over (e)} vectors aregeneralized eigenvectors of the between-class and within-classscattering matrices

$S_{BC} = {{{M_{t}\left( {{\overset{\rightharpoonup}{\mu}}_{t} - \overset{\rightharpoonup}{\mu}} \right)}\left( {{\overset{\rightharpoonup}{\mu}}_{t} - \overset{\rightharpoonup}{\mu}} \right)^{T}} + {{M_{s}\left( {{\overset{\rightharpoonup}{\mu}}_{s} - \overset{\rightharpoonup}{\mu}} \right)}\left( {{\overset{\rightharpoonup}{\mu}}_{s} - \overset{\rightharpoonup}{\mu}} \right)^{T}}}$$S_{WC} = {{\sum\limits_{k = 1}^{M_{t}}\;{\left( {{\overset{\rightharpoonup}{t}}^{(k)} - {\overset{\rightharpoonup}{\mu}}_{t}} \right)\left( {{\overset{\rightharpoonup}{t}}^{(k)} - {\overset{\rightharpoonup}{\mu}}_{t}} \right)^{T}}} + {\sum\limits_{k = 1}^{M_{s}}\;{\left( {{\overset{\rightharpoonup}{s}}^{(k)} - {\overset{\rightharpoonup}{\mu}}_{s}} \right){\left( {{\overset{\rightharpoonup}{s}}^{(k)} - {\overset{\rightharpoonup}{\mu}}_{s}} \right)^{T}.}}}}$The same type of calculation as described above for the raw featurevalues may be performed with the transformed feature values to calculatea ratio of the within-class variance to the between-class variance. Thistransform advantageously maximizes such a ratio, thereby enhancing thediscrimination power of the discriminant model.

In many instances, it is expected that a subset of the features ortransformed features will be sufficient to provide discriminationbetween true skin samples and spoofs. Part of applying the discriminantmodel at block 444 may thus include making a selection of a subset ofthe features or transformed features having sufficient discriminatorypower, in some instances being those features that provide the bestdiscriminatory power. There are a number of techniques that may be usedin different embodiments for selection of the subset of features,including the use of genetic algorithms, neural networks, expertsystems, simulated annealing, and any of a variety ofartificial-intelligence techniques that may permit identification ofthose features having the desired discriminatory power. Such techniquesare sometimes referred to collectively herein as “learning algorithms.”

The application of such techniques is generally well known to those ofskill in this art. For example, a genetic algorithm functions bycreating a population of feature sets, with each set being a subset ofthe total available features. The spoof-detection performance of eachmember of the population is determined. The best-performing members areselected and a new population generated by splitting and combining thefeature sets of the best performers. This process is repeated untilperformance stops improving, with the resultant population defining thedesired feature sets. Such a method is described as “genetic” in analogyto biological systems. The splitting and combining of feature sets isanalogous to biological reproduction of cells and the selection of thebest performing members is analogous to biological selection inreproductive processes.

EXAMPLE

The method of FIG. 4 has been applied by the inventors to evaluate theability of the method to provide good discrimination between spoofs andtrue skin sites. In this example, spoofs and true skin sites wereilluminated under multispectral conditions, with images being acquiredat four distinct wavelengths and under two polarization conditions, across-polarized configuration and a nonpolarized configuration. Therewere thus eight images acquired for each skin site and for each spoof.Each of these eight images was decomposed into three subimages using adual-tree complex wavelet transform as described above, the three imagescorresponding to high, medium, and low frequency components. Inaddition, fourth subimage was generated for each of the images as aratio of the medium-frequency image to the low-frequency image. Each ofthe resulting 32 subimages was subjected to an integral-histogramanalysis in which two scalar features were extracted, one as the sum ofthe intensity of the subimage at a 0.30 percentile with the intensity ata 0.70 percentile and the other as the ratio of the 0.30-percentileintensity to the 0.70-percentile intensity. As previously noted, thesecond of these is a globally illumination-invariant feature while thefirst of these is not.

FIG. 9 shows the integral histogram from one of the subimages, in thisinstance the medium-frequency image plane. The results for a true skinsite are shown with curve 908, which permits comparison with results forthree different types of spoof: a transparent spoof shown with curve904, a semitransparent spoof shown with curve 912, and an opaque spoofshown with curve 916. While there are clearly some differences in theresults, a significant portion of this difference is a consequence ofthe different levels of transparency of the true skin site and differentspoofs. The results of FIG. 10 show haw this effect is mitigated bypresenting an integral histogram of the ratio of the medium-frequencyimage plane to the low-frequency image plane. This provides localinsensitivity to illumination intensity. In that case, genuinedifferences may be discerned between the results of the true skin sitealong curve 1008 and the transparent spoof along curve 1012, thesemitransparent spoof along curve 1016, and the opaque spoof along curve1004.

This calculation of two scalar features from each of the 32 subimagesprovides a total of 64 scalar features that may be subjected to adiscriminant model. In this instance, the 64 scalar features wereorganized into eight groups, each of which has eight members tocorrespond to the eight image planes extracted from the multispectraldata. This grouping is illustrated in Table I. In this table, “P30”refers to the intensity at the 0.30 percentile and “P70” refers to theintensity of the 0.70 percentile.

TABLE I Feature Numbers Elements 1-8 P30/P70 formedium-frequency/low-frequency ratio  9-16 P30 + P70 formedium-frequency/low-frequency ratio 17-24 P30/P70 for high frequency25-32 P30 + P70 for high frequency 33-40 P30/P70 for medium frequency41-48 P30 + P70 for medium frequency 49-56 P30/P70 for low frequency57-64 P30 + P70 for low frequency

The ratio of within-class to between-class variance for these rawfeatures is shown in the results of FIG. 11. Ratios close to unity areindicative of relatively poor discrimination power, and higher ratiosindicate better discrimination power. These results show thatdiscrimination power is spread broadly over the features, although thelower-frequency features at the higher feature numbers re generallybetter. FIG. 12 shows that the discrimination power can be concentratedmore effectively in a smaller number of features by application of theFisher linear discriminant. FIG. 12 shows the ratio of the within-classvariance to between-class variance for the Fisher-transformed features.

In this instance, discrimination power is ever more concentrated in justa few features. Indeed, after transformation, the vast majority of thefeatures have little discrimination power, which is instead concentratedin the last three features. This suggests that discrimination between atrue skin site and a spoof may be accomplished using only threetransformed features.

In fact, even just two of the transformed features prove to besufficient. This is illustrated in FIG. 13, which provides a scatterplot to show the position of the transformed features in atwo-dimensional space spanned by values of the two most significantfeatures. Results for true skin sites are shown with circles, whileresults for different types of spoof are shown with different symbols.It is evident that while these two features alone might not provide gooddiscrimination among the different types of spoof, they show excellentdiscrimination between spoofs and true skin sites. The results for thespoof are clustered in one area of the space and the results for thetrue skin sites are clustered in a different area of the space.

b. Classification of Measurements

Once the system has been trained as described above, it may be used inbiometric applications to identify possible spoofs. A summary isprovided with the flow diagram of FIG. 14 of methods that may be used toclassify samples presented for biometric applications.

The method begins at block 1404 by illuminating a purported skin siteunder multispectral conditions, with the method attempting to classifythe purported skin site as a true skin site or as a spoof. As previouslynoted, the multispectral data that are collected may advantageously beused for biometric identification, but this is not a requirement of theinvention and the methods for classifying the purported skin site may beused in conjunction with any type of biometric identification method, ormay be used in isolation for certain specialized applications. Aplurality of image frames of the purported skin site are extracted fromthe multispectral data at block 1408. These image frames correspond todifferent optical conditions, such as different illuminationwavelengths, different polarization conditions, different illuminationand/or detection angles, and the like. Each frame is decomposed intodifferent frequency components at block 1412, usually using the sametype of decomposition that was used in initial training of the system.

The intensity distribution for a discriminating feature set iscalculated at block 1416. The discriminating feature set is generally asubset of the feature set that was initially analyzed during trainingand corresponds to a set that includes those features determined to havethe desired discriminatory power. These features may comprise rawfeatures or transformed features in different embodiments. For instance,in a system trained with the input data used in the example describedabove, the discriminating feature set might consist of features numbered62, 63, and 64 since these provided virtually all of the discriminatorypower. Under different training scenarios, other features might beincluded in the discriminating feature set.

The specific selection of a subset of features may be useful for anumber of reasons. It may reduce the processing time required to performclassifications after the system has been trained. In addition, thosefeatures that have relatively low discrimination power could add morenoise to the classification and increase the spoof-detection errors.Exclusion of such features from the method may thus improve both thespeed and reliability of classifications.

The values calculated for the discriminating feature set are used atblock 1420 to perform a comparison with the standard feature-set classesto assign the purported skin site to a spoof or nonspoof classificationat block 1424. Such a comparison may proceed in different ways indifferent embodiments. For instance, results like those shown in FIG. 13could be used to define regions of a space spanned by the discriminatingfeatures that correspond to the discriminating feature set. While FIG.13 shows an example in which the space is two-dimensional, the inclusionof a different number of features may result in spaces of three, four,or more dimensions. Assignment of the purported skin site may be madeaccording to a unilateral assignment based on where calculateddistribution for the discriminating feature set maps into the space.

In other cases, statistical techniques may be used to perform acomparison of the results calculated at block 1416 with the trainingresults to determine a confidence level that the purported skin site isa true skin site. The use of statistical techniques in this way permitsthe sensitivity of the method to be adjusted. For instance, relativelylow-security applications might permit validation of a purported skinsite whenever the confidence that it is consistent with a true skin siteis greater than a 75% confidence level; conversely, very-high-securityapplications might impose a confidence level requirement of 99%, withintermediate applications using intermediate confidence levels.

If the purported skin site is classified as a spoof as checked at block1428, an alarm may be issued to prompt further action. The alarm maytake the form of an audible or visible alarm in different embodiments,or may take the form of restricting activity of the person presentingthe purported skin site. In some instances, the alarm may initiatefurther investigation of the purported skin site, the activation oflaw-enforcement personnel, or any of a variety of other responses,depending on the specific application.

In some instances, a check may be made to verify whether thedetermination of the system was correct. That information may be usedfor additional training of the system, particularly in those cases wherethe determination was erroneous either because it identified a true skinsite as a spoof or identified a spoof as a true skin site. A check mayaccordingly be made in some embodiments at block 1436, prompting acalculation of the full intensity distribution feature set for thepurported skin site at block 1440. Such a calculation is not restrictedto the discriminating feature set, but instead duplicates the type ofcalculation performed at blocks 416 and 436 of FIG. 4. This completedistribution feature set is added to the body of reference data thatwere used in deriving the discriminating feature set at block 1444. Thispermits the discriminant model to be applied again at block 1448. Thisapplication is generally the same as the application at block 444 ofFIG. 4, but the inclusion of additional data may result in a differentdetermination of those features that are most discriminating. This maybe particularly true when data have been added to the determination thatyielded an incorrect result with the prior data set.

EXAMPLE

The inventors extended the example described above to testclassification accuracy. Table II below summarizes classification errorsthat may be associated with different feature groups. In one column,results are presented for classification errors that result when aparticular group is excluded and all other groups are included. This iscontrasted with results in another column for classifications thatresult when only a particular group is included.

TABLE II Classification Error (%) Group Only Feature Group ExcludedGroup Included Medium-frequency/low-frequency ratio 1.9 15.0 Highfrequency 1.7 11.0 Medium frequency 1.5 14.7 Low frequency 2.7 5.3 AllData 1.4These results confirm the general result that lower-frequency featuresgenerally provide greater discriminatory power.

Table III below provides results that compare classification errors forfeatures that are insensitive to illumination level with those that aresensitive to illumination level.

TABLE III Classification Intensity Features Error (%) Insensitivity P30,P70 2.4 None (All frequency decomposition levels) P30/P70 9.6 Global(All frequency decomposition levels) P30, P70, P30/P70 12.9 Global andlocal (Ratio of frequency decomposition levels) All features 1.4 MixedWhile it is generally desirable that features be insensitive toillumination intensity, the results of Table III show that theinsensitive features may not be as powerful as the features that havesome sensitivity to illumination intensity. It may thus be advantageousin some embodiments to have a feature set that includes both featuresthat are insensitive to illumination intensity and features that havesome sensitivity to illumination intensity.

Thus, having described several embodiments, it will be recognized bythose of skill in the art that various modifications, alternativeconstructions, and equivalents may be used without departing from thespirit of the invention. Accordingly, the above description should notbe taken as limiting the scope of the invention, which is defined in thefollowing claims.

1. A method of deriving a discrimination feature set for use inidentifying biometric spoofs, the method comprising: illuminating eachof a plurality of true skin sites under a plurality of distinct opticalconditions; extracting true images from light received from each of theplurality of true skin sites, wherein the true images comprisespatially-distributed multispectral images; deriving true-skin featurevalues for each of a plurality of features from the true images tocharacterize the true skin sites; illuminating each of a plurality ofbiometric spoofs under the plurality of distinct optical conditions;extracting spoof images from light received from each of the biometricspoofs, wherein the spoof images comprise spatially-distributedmultispectral images; deriving spoof feature values for each of theplurality of features from the spoof images to characterize thebiometric spoofs; and comparing the derived true-skin feature valueswith the derived spoof feature values to select a subset of the featuresto define the discrimination feature set.
 2. The method recited in claim1 wherein: each of the true-skin images and each of the spoof imagescorresponds to an image under one of the plurality of distinct opticalconditions.
 3. The method recited in claim 1 wherein: deriving thetrue-skin feature values further comprises decomposing each of thetrue-skin images into a plurality of different spectral frequencycomponents; and deriving the spoof feature values further comprisesdecomposing each of the spoof images into the plurality of differentspectral frequency components.
 4. The method recited in claim 3 whereindecomposing each of the true-skin images and decomposing each of thespoof images comprises performing a wavelet decomposition.
 5. The methodrecited in claim 3 wherein: deriving the true-skin feature valuesfurther comprises calculating a ratio of a first of the differentspatial frequency components for the true-skin images to a second of thedifferent spatial frequency components for the true-skin images; andderiving the spoof feature values further comprises calculating a ratioof a first of the different spatial frequency components for the spoofimages to a second of the different spatial frequency components for thespoof images.
 6. The method recited in claim 3 wherein: deriving thetrue-skin feature values further comprises calculating an intensitydistribution for each of the different spatial frequency components forthe true-skin images; and deriving the spoof feature values furthercomprises calculating an intensity distribution for each of thedifferent spatial frequency components for the spoof images.
 7. Themethod recited in claim 6 wherein at least one of the features issubstantially invariant to illumination intensity.
 8. The method recitedin claim 7 wherein the at least one of the features comprises a ratio ofan intensity at a first predetermined percentile of an intensitydistribution to an intensity at a second predetermined percentile of theintensity distribution.
 9. The method recited in claim 7 wherein atleast a second of the features varies with illumination intensity. 10.The method recited in claim 9 wherein: the at least one of the featurescomprises a ratio of an intensity at a first predetermined percentile ofan intensity distribution to an intensity at a second predeterminedpercentile of the intensity distribution; and the at least a second ofthe features comprises a difference between the intensity at the firstpredetermined percentile and the intensity at the second predeterminedpercentile.
 11. The method recited in claim 6 wherein at least one ofthe features varies with illumination intensity.
 12. The method recitedin claim 1 wherein: the true skin sites and the biometric spoofs defineseparate classes; and comparing the derived true-skin feature valueswith the derived spoof feature values comprises calculating ratios ofwithin-class variance to between-class variance for a quantity derivedfrom the features.
 13. The method recited in claim 12 wherein thequantity derived from the features comprises a Fisher lineardiscriminant transform of the features.
 14. The method recited in claim1 wherein comparing the derived true-skin feature values with thederived spoof feature values to select the subset of the featurescomprises applying a learning algorithm to the features to select thesubset of the features.
 15. The method recited in claim 14 wherein thelearning algorithm comprises a genetic algorithm.
 16. Acomputer-readable storage medium having a computer-readable programembodied therein for directing operation of a computational device toderive a discrimination feature set for use in identifying biometricspoofs, the computational device including a processor in communicationwith a storage device, the computer-readable program including:instructions for retrieving, with the processor from the storage device,first data representing properties of first light reflected from each ofa plurality of true skin sites under a plurality of distinct opticalconditions; instructions for extracting a plurality of true-skin imagerepresentations from the first data for each of the true skin sites,wherein the true-skin images are spatially-distributed multispectralimages; instructions for retrieving, with the processor from the storagedevice, second data representing properties of second light reflectedfrom each of a plurality of biometric spoofs; instructions forextracting a plurality of spoof image representations from the seconddata for each of the biometric spoofs, wherein the spoof imagerepresentations comprise spatially distributed multispectral images; andinstructions for comparing, with the processor, the derived true-skinfeature values with the derived spoof feature values to select a subsetof the features to define the discrimination feature set.
 17. Thecomputer-readable storage medium recited in claim 16 wherein: each ofthe true-skin image representations and spoof image representationscorresponds to an image under one of the plurality of distinct opticalconditions.
 18. The computer-readable storage medium recited in claim 16wherein: the instructions for deriving the true-skin feature valuesfurther comprise instructions for decomposing each of the true-skinimage representations into a plurality of different spatial frequencycomponents; and the instructions for deriving the spoof feature valuesfurther comprise instructions for decomposing each of the spoof imagerepresentations into the plurality of different spatial frequencycomponents.
 19. The computer-readable storage medium recited in claim 18wherein the instructions for decomposing each of the true-skin imagerepresentations and the instructions for decomposing each of the spoofimage representations comprise instructions for performing a waveletdecomposition.
 20. The computer-readable storage medium recited in claim18 wherein: the instructions for deriving the true-skin feature valuesfurther comprise instructions for calculating a ratio of a first of thedifferent spatial frequency components for the true-skin imagerepresentations to a second of the different spatial frequencycomponents for the true-skin image representations; and the instructionsfor deriving the spoof feature values further comprise instructions forcalculating a ratio of a first of the different spatial frequencycomponents for the spoof image representations to a second of thedifferent spatial frequency components for the spoof imagerepresentations.
 21. The computer-readable storage medium recited inclaim 18 wherein: the instructions for deriving the true-skin featurevalues further comprise instructions for calculating an intensitydistribution for each of the different spatial frequency components forthe true-skin image representations; and the instructions for derivingthe spoof feature values further comprise instructions for calculatingan intensity distribution for each of the different spatial frequencycomponents for the spoof image representations.
 22. Thecomputer-readable storage medium recited in claim 21 wherein at leastone of the features is substantially invariant to illuminationintensity.
 23. The computer-readable storage medium recited in claim 22wherein the at least one of the features comprises a ration of anintensity at a first predetermined percentile of an intensitydistribution to an intensity at a second predetermined percentile of theintensity distribution.
 24. The computer-readable storage medium recitedin claim 22 wherein at least a second of the features varies withillumination intensity.
 25. The computer-readable storage medium recitedin claim 24 wherein: the at least one of the features comprises a ratioof an intensity at a first predetermined percentile of an intensitydistribution to an intensity at a second predetermined percentile of theintensity distribution; and the at least a second of the featurescomprises a different between the intensity at the first predeterminedpercentile and the intensity at the second predetermined percentile. 26.The computer-readable storage medium recited in claim 21 wherein atleast one of the features varies with illumination intensity.
 27. Thecomputer-readable storage medium recited in claim 16 wherein: the trueskin sites and the biometric spoofs define separate classes; and theinstructions for comparing the derived true-skin feature values with thederived spoof feature values comprise instructions for calculatingratios of within-class variance to between-class variance for a quantityderived from the features.
 28. The computer-readable storage mediumrecited in claim 27 wherein the quantity derived from the featurescomprises a Fisher linear discriminant transform of the features. 29.The computer-readable storage medium recited in claim 16 wherein theinstructions for comparing the derived true-skin feature values with thederived spoof feature values to select the subset of the featurescomprise instructions for applying a learning algorithm to the featuresto select the subset of the features.
 30. The computer-readable storagemedium recited in claim 29 wherein the learning algorithm comprises agenetic algorithm.